• Define and execute the roadmap for LLM and multimodal AI across the organization • Make architectural decisions across prompting, fine-tuning, retrieval (RAG), and multimodal system design • Set standards for model evaluation, safety, and deployment • Build, manage, and mentor a team of AI/ML researchers, engineers, clinicians • Establish clear goals, execution plans, and performance expectations for the team • Create a culture of rigor, documentation, and accountability—especially around clinical risk • Own safety frameworks for AI systems, including hallucination mitigation and fail-safe design • Ensure human-in-the-loop systems are appropriately designed and implemented • Drive alignment of AI systems with SaMD principles and regulatory expectations • Guide development of systems combining text, voice, image, and structured data • Partner with product, engineering, and clinical teams to translate research into deployable features

About Voyage AI Team at MongoDBVoyage AI team in MongoDB is building a best-in-class, general-purpose, domain-specific, and fine-tuned embedding models and rerankers to enable accurate, efficient unstructured data search and retrieval for RAG, recommendation, semantic search, and more. It is backed by a strong team of AI researchers from Stanford, MIT, Berkeley, Princeton, and CMU, who have conducted over five years of cutting-edge research on training embedding models. Voyage AI was acquired by MongoDB recently, and is now integrating the SOTA embedding models with MongoDB's data platform to create powerful end-to-end solutions. Position OverviewWe are seeking a Staff Research Scientist to join our team and contribute to the development of next-generation AI models. This position offers a unique opportunity to work on challenging problems at the intersection of machine learning research and practical deployment of large neural networks. This role can be based out of our Palo Alto office, or remotely in the United States. Responsibilities - Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers - Innovate in next-generation information retrieval and LLM agent paradigm - Collaborate closely with other research scientists and research engineers as well as peers across the organization Qualifications - PhD degree in Computer Science or related field - A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications in top venues - Strong background in machine learning, deep learning, and natural language processing - Experience building complex neural networks for language and visual understanding - Capable of conducting rigorous empirical studies to validate theoretical results - Excellent leadership, problem-solving, and communication skills What We Offer - Opportunity to work on real-world problems at the cutting edge of AI research - Opportunity to utilize research vision to innovate the entire company and make real-world impact - Exposure to the full lifecycle of AI model development, from research to production - Our compensation (base + equity) for this position is competitive with frontier AI labs About MongoDBMongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure. With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software. Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world! MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter. MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Req ID: 3263258184 MongoDB’s base salary range for this role is posted below. Compensation at the time of offer is unique to each candidate and based on a variety of factors such as skill set, experience, qualifications, and work location. Salary is one part of MongoDB’s total compensation and benefits package. Other benefits for eligible employees may include: equity, participation in the employee stock purchase program, flexible paid time off, 20 weeks fully-paid gender-neutral parental leave, fertility and adoption assistance, 401(k) plan, mental health counseling, access to transgender-inclusive health insurance coverage, and health benefits offerings. Please note, the base salary range listed below and the benefits in this paragraph are only applicable to U.S.-based candidates. MongoDB’s base salary range for this role in the U.S. is: $137,000—$270,000 USD

• Propose and implement engineering solutions to ensure delivery of functional, reliable, secure, and performance-optimal GPU clusters to internal researchers. • Design, develop and maintain engineering solutions to understand the pain points of validating, monitoring and operating GPU clusters at scale. • Research in traditional AIOps and the emerging Agentic AI, and leverage it to further reduce the operation toil. • Participate in on-call support for systems, platforms built and owned by the team.

• Design and implement advanced multi-step reasoning agents (tool use, planning, reflection, self-improvement loops) • Develop frameworks for multi-agent coordination and task decomposition • Improve reliability, latency, and cost efficiency of agent execution • Architect short-term and long-term memory subsystems (episodic, semantic, retrieval-based, hybrid) • Define and implement evaluation frameworks for agent performance (task success, reasoning quality, robustness) • Translate latest community research ideas into production-grade systems